Proteomic and phospho-proteomic data derived from mass spectrometry experiments offer unique opportunities to interrogate cellular signaling pathways and networks in an unbiased and comprehensive manner. The role of phosphorylation linked signaling processes in the development of Alzheimer's Disease (AD) is well-established, yet little is known about gender, age/disease stage,tissue, and etiology based variations in this signaling. This supplement will provide novel data that pertains to this cellular signaling, and the parent R01 provides novel computational tools for systems-level analysis of proteomic and phosphoproteomic data essential to characterizing the signaling landscape of Alzheimer's Disease. In the parent award, R01-LM-012980 (funded under PAR-18-896), we are developing enabling systems and network-based analyses of phosphoproteomic data in the context of a broad range of biomedical problems. Our project is advancing the field through development of algorithms for predicting kinase-substrate associations, inference of kinase activity, and identification of context-specific changes in cellular signaling. An opportunity to expand the focus of this award around Alzheimer's disease models exists due to an emerging collaboration with Dr. Mark Chance (proteomics expert, co-investigator for parent award) and Dr. Xin Qi (neurodegenerative disease expert, consultant for parent award). These co-investigators recently received supplemental funding from NIGMS/NIA (3 R01 GM117208-03S1) to collect pivotal proteomics and phosphoproteomics data on brain tissue from the 5XFAD AD mouse model at various stages of disease development. The temporal progression of the plaque-centered disease in the 5XFAD mouse model highlights the initial development of neuroinflammation “proteomic phenotypes” followed by neurodegeneration-linked molecular phenotypes, including specific upregulation of many Alzheimer's related proteins like synucleins and tau from data examined in the hippocampus. In this supplement under (NOT-AG-18-008), we will leverage our network-based algorithms to accelerate AD research by further characterizing the specific signaling changes that underlie neuronal degeneration in a tauopathy mouse model (PS19) as a function of gender, stage of development, and tissue type to provide complementary basic science systems level understanding of disease progression in AD mouse models. Using these mouse models , we will identify signaling networks composed of specific kinases, substrates, and phosphorylation sites that exhibit dysregulation in male and female mice representing different etiologies of Alzheimer's Disease (Supplement Aim 1) and potential biomarkers that can aid in the diagnosis and prognosis of Alzheimer's Disease at different stages (Supplement Aim 2).